Robust activity recognition using wearable IMU sensors

An orientation transformation (OT) algorithm is presented that increases the effectiveness of performing activity recognition using body sensor networks (BSNs). One of the main limitations of current recognition systems is the requirement of maintaining a known, or original, orientation of the sensor on the body. The proposed OT algorithm overcomes this limitation by transforming the sensor data into the original orientation framework such that orientation dependent recognition algorithms can still be used to perform activity recognition irrespective of sensor orientation on body. The approach is tested on an orientation dependent activity recognition system which is based on dynamic time warping (DTW). The DTW algorithm is used to detect the activities after the data is transformed by OT. The precision and recall for the activity recognition for five subjects and five movements was observed to range from 74% to 100% and from 83% to 100%, respectively. The correlation coefficient between the transformed data and the data from the original orientation is above 0.94 on axis with well-defined patterns.

[1]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[2]  Surapa Thiemjarus,et al.  A Device-Orientation Independent Method for Activity Recognition , 2010, 2010 International Conference on Body Sensor Networks.

[3]  Hassan Ghasemzadeh,et al.  A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[4]  Christos Faloutsos,et al.  Stream Monitoring under the Time Warping Distance , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[5]  Eamonn Keogh.  Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[6]  Gregory J. Pottie,et al.  Self-calibration of sensor misplacement based on motion signatures , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[7]  Tae-Seong Kim,et al.  A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer , 2010, IEEE Transactions on Information Technology in Biomedicine.

[8]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[9]  Jun Yang,et al.  Toward physical activity diary: motion recognition using simple acceleration features with mobile phones , 2009, IMCE '09.

[10]  Reza Lotfian,et al.  An ultra-low power hardware accelerator architecture for wearable computers using dynamic time warping , 2013, 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE).